class_docstr: Leave One Feature Out Importance
  Given a model and cross-validation scheme, calculates the feature importances.
  
  Parameters
  ----------
  dataset: LOFO Dataset object
  scoring: string or callable
      Same as scoring in sklearn API
  model: model (sklearn API), optional
      Not trained model object
  fit_params : dict, optional
      fit parameters for the model
  cv: int or iterable
      Same as cv in sklearn API
  n_jobs: int, optional
      Number of jobs for parallel computation
  cv_groups: array-like, with shape (n_samples,), optional
      Group labels for the samples used while splitting the dataset into train/test set. 
      Only used in conjunction with a “Group” cv instance (e.g., GroupKFold).
code: def __init__(
      self,
      dataset,
      scoring,
      model=None,
      fit_params=None,
      cv=4,
      n_jobs=None,
      cv_groups=None,
  ):
      self.fit_params = fit_params if fit_params else dict()
      if model is None:
          model, dataset.df, categoricals, dataset.y = infer_model(
              dataset.df, dataset.features, dataset.y, n_jobs
          )
          self.fit_params["categorical_feature"] = categoricals
          n_jobs = 1
  
      self.model = model
      self.dataset = dataset
      self.scoring = scoring
      self.cv = cv
      self.cv_groups = cv_groups
      self.n_jobs = n_jobs
      if self.n_jobs is not None and self.n_jobs > 1:
          warning_str = (
              "Warning: If your model is multithreaded, please initialise the number"
              "of jobs of LOFO to be equal to 1, otherwise you may experience performance issues."
          )
          warnings.warn(warning_str)
  
file_path: lofo-importance/lofo/lofo_importance.py
imports: import numpy
  from sklearn.model_selection import cross_validate
  from tqdm.autonotebook import tqdm
  import warnings
  from lofo.infer_defaults import infer_model
  from lofo.utils import lofo_to_df, parallel_apply
label_desc: Use image node_id 1 to create a new LOFOImportance object  with example: obj = LOFOImportance(dataset, scoring, model, fit_params, cv, n_jobs, cv_groups)